{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalprice</th>\n",
       "      <th>unitprice</th>\n",
       "      <th>position</th>\n",
       "      <th>rooms</th>\n",
       "      <th>size</th>\n",
       "      <th>orientation</th>\n",
       "      <th>decoration</th>\n",
       "      <th>elevator</th>\n",
       "      <th>area</th>\n",
       "      <th>height</th>\n",
       "      <th>year</th>\n",
       "      <th>buildtype</th>\n",
       "      <th>follow</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920.0</td>\n",
       "      <td>77188</td>\n",
       "      <td>天悦龙庭二期</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>119.19</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>中</td>\n",
       "      <td>2005</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>13</td>\n",
       "      <td>宝安</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340.0</td>\n",
       "      <td>51909</td>\n",
       "      <td>大益广场</td>\n",
       "      <td>2室2厅</td>\n",
       "      <td>65.50</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>西乡</td>\n",
       "      <td>低</td>\n",
       "      <td>2002</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>160</td>\n",
       "      <td>宝安</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660.0</td>\n",
       "      <td>73884</td>\n",
       "      <td>中熙香缇湾</td>\n",
       "      <td>4室2厅</td>\n",
       "      <td>89.33</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>西乡</td>\n",
       "      <td>中</td>\n",
       "      <td>2009</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>226</td>\n",
       "      <td>宝安</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>76657</td>\n",
       "      <td>深业新岸线三期</td>\n",
       "      <td>4室1厅</td>\n",
       "      <td>150.02</td>\n",
       "      <td>东南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>中</td>\n",
       "      <td>2008</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>29</td>\n",
       "      <td>宝安</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570.0</td>\n",
       "      <td>52127</td>\n",
       "      <td>碧海富通城一期</td>\n",
       "      <td>3室2厅</td>\n",
       "      <td>109.35</td>\n",
       "      <td>南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>碧海</td>\n",
       "      <td>中</td>\n",
       "      <td>2006</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>54</td>\n",
       "      <td>宝安</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalprice  unitprice  position rooms    size orientation decoration  \\\n",
       "0       920.0      77188   天悦龙庭二期   3室2厅  119.19          西南         精装   \n",
       "1       340.0      51909     大益广场   2室2厅   65.50          西南         精装   \n",
       "2       660.0      73884    中熙香缇湾   4室2厅   89.33          西南         精装   \n",
       "3      1150.0      76657  深业新岸线三期   4室1厅  150.02          东南         精装   \n",
       "4       570.0      52127  碧海富通城一期   3室2厅  109.35           南         精装   \n",
       "\n",
       "  elevator  area height  year buildtype  follow city  \n",
       "0      有电梯  宝安中心      中  2005        塔楼      13   宝安  \n",
       "1      有电梯    西乡      低  2002      板塔结合     160   宝安  \n",
       "2      有电梯    西乡      中  2009      板塔结合     226   宝安  \n",
       "3      有电梯  宝安中心      中  2008        塔楼      29   宝安  \n",
       "4      有电梯    碧海      中  2006      板塔结合      54   宝安  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "sns.set_style('whitegrid')\n",
    "%matplotlib inline\n",
    "sns.set_context(\"paper\")\n",
    "\"\"\"数据加载\"\"\"\n",
    "house = pd.read_excel('house.xls', index_col=None)\n",
    "house=house[~house['height'].isin(['10层1994年建板', '11层2002年建板塔结合', '15层2003年建塔', '16层2002年建板塔结合',\n",
    "       '17层2003年建板塔结合', '1层2001年建板塔结合', '1层2013年建板', '1层2014年建板',\n",
    "       '20层2003年建塔', '21层2011年建板', '23层2011年建板', '26层2009年建板塔结合',\n",
    "       '27层2011年建板塔结合', '30层2010年建板', '32层2013年建板塔结合', '33层1996年建塔',\n",
    "       '6层1998年建板', '7层1993年建板', '8层2001年建板塔结合', '8层2009年建板塔结合',\n",
    "       '9层1997年建板塔结合', '9层2011年建板'])]\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>main_room</th>\n",
       "      <th>sub_room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   main_room  sub_room\n",
       "0          3         2\n",
       "1          2         2\n",
       "2          4         2\n",
       "3          4         1\n",
       "4          3         2"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 分割户型\n",
    "huxing_split = pd.DataFrame((re.split('[\"室\",\"厅\"]',x) for x in house.rooms),index=house.index,columns=['main_room','sub_room', 'a'])\n",
    "huxing_split.drop(['a'],axis=1,inplace=True)\n",
    "huxing_split['main_room']=pd.to_numeric(huxing_split['main_room'],errors='coerce')\n",
    "huxing_split['sub_room']=pd.to_numeric(huxing_split['sub_room'],errors='coerce')\n",
    "huxing_split.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalprice</th>\n",
       "      <th>unitprice</th>\n",
       "      <th>position</th>\n",
       "      <th>size</th>\n",
       "      <th>orientation</th>\n",
       "      <th>decoration</th>\n",
       "      <th>elevator</th>\n",
       "      <th>area</th>\n",
       "      <th>height</th>\n",
       "      <th>year</th>\n",
       "      <th>buildtype</th>\n",
       "      <th>follow</th>\n",
       "      <th>city</th>\n",
       "      <th>main_room</th>\n",
       "      <th>sub_room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920.0</td>\n",
       "      <td>77188</td>\n",
       "      <td>天悦龙庭二期</td>\n",
       "      <td>119.19</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>中</td>\n",
       "      <td>2005</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>13</td>\n",
       "      <td>宝安</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340.0</td>\n",
       "      <td>51909</td>\n",
       "      <td>大益广场</td>\n",
       "      <td>65.50</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>西乡</td>\n",
       "      <td>低</td>\n",
       "      <td>2002</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>160</td>\n",
       "      <td>宝安</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660.0</td>\n",
       "      <td>73884</td>\n",
       "      <td>中熙香缇湾</td>\n",
       "      <td>89.33</td>\n",
       "      <td>西南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>西乡</td>\n",
       "      <td>中</td>\n",
       "      <td>2009</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>226</td>\n",
       "      <td>宝安</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>76657</td>\n",
       "      <td>深业新岸线三期</td>\n",
       "      <td>150.02</td>\n",
       "      <td>东南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>中</td>\n",
       "      <td>2008</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>29</td>\n",
       "      <td>宝安</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570.0</td>\n",
       "      <td>52127</td>\n",
       "      <td>碧海富通城一期</td>\n",
       "      <td>109.35</td>\n",
       "      <td>南</td>\n",
       "      <td>精装</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>碧海</td>\n",
       "      <td>中</td>\n",
       "      <td>2006</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>54</td>\n",
       "      <td>宝安</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalprice  unitprice  position    size orientation decoration elevator  \\\n",
       "0       920.0      77188   天悦龙庭二期   119.19          西南         精装      有电梯   \n",
       "1       340.0      51909     大益广场    65.50          西南         精装      有电梯   \n",
       "2       660.0      73884    中熙香缇湾    89.33          西南         精装      有电梯   \n",
       "3      1150.0      76657  深业新岸线三期   150.02          东南         精装      有电梯   \n",
       "4       570.0      52127  碧海富通城一期   109.35           南         精装      有电梯   \n",
       "\n",
       "   area height  year buildtype  follow city  main_room  sub_room  \n",
       "0  宝安中心      中  2005        塔楼      13   宝安          3         2  \n",
       "1    西乡      低  2002      板塔结合     160   宝安          2         2  \n",
       "2    西乡      中  2009      板塔结合     226   宝安          4         2  \n",
       "3  宝安中心      中  2008        塔楼      29   宝安          4         1  \n",
       "4    碧海      中  2006      板塔结合      54   宝安          3         2  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del house['rooms']\n",
    "house=pd.merge(house,huxing_split,right_index=True, left_index=True)\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#用来正常显示中文标签\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "#用来正常显示负号\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HASEE\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(house['totalprice'])\n",
    "plt.savefig('总价分布.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析装修影响\n",
    "var = 'decoration'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-装修.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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//e2DlrNosBnS44CJwNlm9hOCvoeJBLf/lBjkcjl++ctfsmvXLsaMGaN5DimjtZTSa8aMGbz88su7y0cffXSC0VSHwWoO5wJ3Ae8muIfD3cCNgBrjYtLQ0MBZZ51FTU0NZ511luY5pEzxXhx7K0v1evrp0hH6v/71rxOKpHoMNkO6BWgxs0fcfdCb+0jl5HI51q9fr1pDCr3tbW/j1VdfLSlLOsyaNYtVq1btLmtF5PLuBHe6mU0H3gm4u78Yb0gi6bR169ZByyJpUs5Q1sUEo5ROAm4ws0vjDSnbWlpaaGtr052oUkg3+0mvxx57rKT86KOPJhNIFSknOcxz9zPc/TJ3bwb2upS3vDUdHR20trZSKBRobW1l06ZNSYckQ1BXVzdoWaqX5hhFlZMctpjZh81sqpnNBbbEHVRWtbS07J7n0NPTo9pDygxc/uTqq69OJhAZMq1OEFVOevw0cBlwMdAOfHOwjc1sArAUGAe0Au8CjgHWuvt1ZnYCcDXwarjPncByoA9Y5u4P79+fkn5r1qyhr68PgL6+PlavXs2ll6oVLy2OP/74knJTU1NCkchQTZ8+vWTi2/Tp0xOLpVqUU3P4gbt/2d3PdPcvAz/cx/bHA//q7p8EcsBh7j4fONLM6oElwDnADcB5wAXAinCbTA/Ree9731tSPu644xKKRPbHQw89VFJ+5JFHEopEhuqjH/1oSXn+/PkJRVI99poczOydZnYOcJyZzQu/zgNqB9uhuz8KvGxmPwB+ATwRPtUGHAF0ufsWIA/MAI7qt82bb+WPSbu2traS8jPPPJNQJLI/vvOd75SU1ayUHrfccktJ+cYbb0wokuoxWLPS2whuCVoXPtYAXcAnythvJ8EH/nfDr+JrHQh0Dyhv7Pd7k8sNvL29vdxNU2Pbtm2R8kj8O0eq/utiFcs6fumwpz6HrB+7wSbBPQY8ZmaHuHvZs6LD2kbe3e8ys4XALOBmoAm4F+gysykECedFYB1wqpk9D0wq93UaGxvL3TQ1JkyYUDL8ccKECSPy7xypRo8eza5du0rKOn7pUFdXV5Ig6urqRuSxG8oNxPbZ5+DufzfE1/8dwXyIu4GfAE+G37/g7hsIbh50E7AofFxJsETHfQT9EJk1fvz4kvKECRMSikT2x0EHHVRSnjSp7GsdSZhGmkVVfDCvuz8HnDngx8v6Pf8sMLC3Z3Gl40ijgfMaOjo6EopE9sfmzZtLypqnkh4DE/nARJ9F5YxWEhEZ0XSznyglBxHJPN3sJ0rJQUQyb+CkN02CU3KoKgM7oNUhLTI8Fi1aVFK+5JJLEoqkeig5VBGt6imSjDVr1pSU+9/bIauUHKqIqrbpNrCmN3HixIQikaEamBxWr16dUCTVQ8mhilx++eUl5W9+c9A1DqXKDKzpDZzxLtWruODl3spZpORQRSZPLl095OCDD04oEhHJOiWHKnLttdcOWhYRGS5KDlVk7dq1JeUnn3wyoUhEJOuUHEREJELJQUREIpQcREQkQslBREQilByqSE1NzaBlEZHhouRQRQqFwqBlEZHhouQgIiIRSg4iIhKh5CAiIhFKDiKSeQcccMCg5SxSchCRzNu5c+eg5SxScqgihx9++KBlEZHhMqbSOzSzvwZ+SJB4vg80AccAa939OjM7AbgaeBW4GNgJLAf6gGXu/nClY0qLgw8+mJdeeml3eeAS3iIiwyWOmsP7gI8Af0vw4X+Yu88HjjSzemAJcA5wA3AecAGwItwmF0M8qdHW1lZSfuaZZxKKRESyruI1B3f/MYCZzQV+Bfy/8Kk24Aigy923mFkemBc+d3P4+Ga5r9Pe3l6ZgKtcVv7OkUrHL72yfuwqnhwAzGwKcAmwFHhbv9c6EOgeUN7Y71fLbkdpbGx864GmQFb+zpFKxy+9RuKxy+fzZW9b8WYlMxsH/BtBcvgVMCt8qglYD3SFyeP9wIvAU8CpZjYKmFTpeEREZOjiqDlcRtB89CXgFeBJM7sb+K27bzCza4CbgAKwkKAp6V+A+wj6IUREJGFx9Dl8C/jWgB8v6/f8s8D8Ac8vrnQcIiKy/zTPQUREIpQcREQkQslBREQilBxERCRCyUFERCKUHEREJELJQUREIpQcREQkQslBREQilBxEJPNGjx49aDmLlBxEJPOOO+64kvL73ve+hCKpHkoOIpJ569atG7ScRUoOIpJ53d3dJeUdO3YkFEn1UHIQEZEIJQcREYlQchARkYhY7iGdFVu3bo20VVZaR0dHxfY1duxY6uvrK7Y/ERm5lBz2U29vLwsWLGDbtm2xvs68efMqtq+JEyeyYsUKxozRYR+OxA5K7nHRhVn8agqFQtIxDFk+ny80NTUlHUbF36AXX3wxr7322u7y1KlTuf766yu2/zS+QePQ29vL3LlzY0/slabkHkjj8auWY5fP52lqaqopZ1slhypz4okn7v7+8ccfTzCSkS2OK89bbrmF1tbW3eW5c+fymc98pmL7V3L/i0ofvz3V0O+5556K7b9ajt1QkkO2L0Gq0CGHHMJrr73G1KlTkw5lRKuvr6/4yXrZZZeVJIevfvWrFd2//EWlj9+8efNKksGCBQtoaGio2P7TKJbRSmZ2hpndEn7/HTO7y8y+HJZPMLM1ZvYzM6szswPM7J5wmw/FEU+a3HjjjQAVbU6S4dPc3AwEtQZJjy9+8Ysl5UWLFiUUSfWoeHIws3cBZwK1ZtYIHObu84EjzaweWAKcA9wAnAdcAKwIt8lVOh6R4XTRRRcBVLQ5SYaHEnupiicHd/8D8IOweBTwRPh9G3AE0OXuW4A8MGPANm9WOh4RkXIosZeKu8+hf4/RGODAfj8rljf222ZyuTtub29/y8FVozfeeAOAP/7xj2zcuHEfW0u10fFLLx27UnEnhzzwaeBmoAm4F+gysynAscCLwDrgVDN7HphU7o4bGxsrH20VKI6tPvroozPfIZZGOn7plYVjl8/ny9421uUz3H0z8KSZ3Q284O4bgGuAm4BF4eNK4N3AfQT9ECIikrBYag7uvh44P/x+GbCs33PPAvMH/MriOOIQEZH9o4X3REQkQslBREQilBxERCRCy2eISCw6OztTdbvNzZs3lzymxbhx46irq6v4fkd8ctAbdHjE9QbV8YtfHMeus7OTjy34GNs607NyatGFF16YdAhDMrFuInfecWfFj+GITg7BG/RctnVuTTqUIUvfG7SeO+/4j4q+QTs7O/n4gvls6dxesX0OlzQdvwPrJrD8jrsqeux27NjBts5t7DptF4yr2G7j10e6Gtt3wLaHtrFjxw4lh6EI3qBb6Tr2bAq145MOp3yFPqhJzzu0pqcL1t1X8Tfojh072NK5nauP38KksX0V22/cdhVgdFmLIifvje5RXPE0sXy4AEFiSNGpJ38xopNDUaF2PIUDJiYdhuynSWP7OHhs+u47kg7pSboyvNJzeSoiIsMmEzWHmp70tVmnif6/slfpGUuQTjH+f0d0cti1axcA49f9IuFIsqH4/660N7tT0oCfQnH9b4vvhdEPjY5l/1IqjnNvRCeH0aODN2bXsR+hUDsh4WhGrpqe7Yxf94vd/+9KKb7hL3/6oIruV6Iq/eFSfC+kbrRS2uwIEnClzz0Y4cmhqFA7QR3SKVR8wy85/k0OUod0LN7sruHypw+K5cMlldI2lDVGmUgOkm4HjS1otFLKjBs3jol1E9n2UPomwaXNxLqJjBtX+epZJpJDTU9X0iEMTRrnOcToje5RpGnIZdrmOcShrq6OO++4M3Wz2y+88EJuvfVWJk8u+6aUidPyGfshuHqph3X3JR3KiDexrr7iVy/jxo3jwLoJXPF0RXcrAxxYNyGWK8+6urp4JtbFbPLkySP2TnBDMaKTQ3D18h+6ehkGcVy91NXVsfyOu3T8YhbXlaek24hODqCrl7TT8RNJRnoatkVEZNgoOYiISMSIb1aK09atW+nu7q7oPuO8H8DYsWOpr6+v+H5FhpvOvfhVRXIws8nAz4FO4Cp3/13CIe1Tb28vCxYsYNu2eMZxx3E/gIkTJ7JixQrGjKmKw56oOD5cQB8ww0Hn3vColki/BnwXeBL4EXBRsuHs25gxY7jjjjti+YDp7e2N5U00duzYVL054xL3hwvoAyZOOveGR7VEexTwhLvvNLPUjFusr6/XlVwKxfnhAvqAGQ469+JXLe+0bqA4p7SsweH5fD6+aEREMq5aksNTwClm9l/A2H1t3NTUlJLFCURE0qlahrL+BPg4Qaf0dxOORUQk82oKBa12KSIipaql5iAiIlVEyUFERCKUHEREJELJQUREIpQcUsDMapOOQeJhZiebmYZmV7Gsnn8arZQAM/sZ8DngfwGfB/4J+B7wVeBF4F+B84GPheW/B1YBv3f33yQQcmaY2QeB44BbgTXAFuAVoI1gaZefExyPTxFcXD1EcOz+AXiG4H6mPwdy7r4z3Od97n72Hl5rFnAj8CV3Xx3vXyZFZRzjFnT+Vc0kuKzpJZgR/nZgEnAuMAE4HFgEnA4sB+rD52cABuwAZiUQbyaY2QyC/+904BjgBXc/f8A23cBE4K+AdxDM7i8eu98BfwvcDVxgZoTbHmFmXwFGu/t3w/28E7iU4ALhWjMb5e4PxvwnZt6+jrGZXY/OP0DJYdiZ2SVAM8EV54eAvya4QvmCu682sweAtcC3gD8CtwFvApuAhUnEnDHvILj67wPODGft9wJOMHt/DrAL+DJwJ/AR4CJ3/72ZjSOo9W0AngOuIbgAOBPIAy8BmNk5wGLgSuBI4Gbga2b2SeB77v7MsPyl2TXYMf4CQcLO/PmnPodh5u4/BFrd/W8IPmgOBc4GrjSz+cAU4L3A1wlmjt9DcCX6A6A1q+2fw8Hdnyf4/x8IbAT+C3iM4Ph0hleYDxKsGnwZsBK4HlhqZh9w9+KV5cvAV4CDCK5QfwocARxkZocA7wK+BLwHODn8nZ8C1wEHxP+XZte+jjHBMdP5h2oOiTGz6QTV1f8ETgW+DxxCcHXyW+AzwFRgLvCSu/+3mZ1GcIUjMTCzIwk+xF8gSABO0Fx0EvB4v03PBH5DUCs4gqDP6DQz+z3wWYIr0ouBR4Bcv9+71t3zwNVm9p9EL84+5+7PVfrvkr8o4xjn0PkHKDkMOzObQlCtvR14gKBjuougbXoZQVv2s+E2NxNcXT5jZrcC6wiuSNXsEI+zgWsJjsFrwHhgPcFV/hlmdgzwQYIPiIMJrkAhSOgfcfdOM3sZaCf48GkAbnP328ysWEss2uju5xYLZnYlQd+FxGvQYxw+p/MPNSsl4RyCN91JQAFY6O4nE7w5R7n73wO4+3rgk8AKgivQZe4+W+3R8XH37wN/DovnAQ8TXFWOBt5NcDV5vrtfCPS6+8nhsbsd6Av7HM4laJK4luDD/utm9ijwzwNe7oNm9mjxi2B0jMRsX8dY599fKDkMM3e/2d2Xu3svQXK4OfxwWAoU2zNrw7bpy4DXgQ6gYGafTyLmjKklGMXyDPCPBFeRswk6kH/i7k+E243v98F+AVAb9jlcD5zs7qcAO4F/ChPIP1BaU3+qmFzC528j+ICS+O31GJvZ0ej8AzTPoaqZ2Wh335V0HFn1Vv//YedlTXG+g1SfwY5x1s8/JQcREYlQs5KIiEQoOYiISISSg2SWmRUnHu5ruzlmdu6An+3xd8OF9H4Ufj/dzM6sXMQiw0fzHET24S2seTSdYMLcyspFIzI8lBwk6y4ysy8CjxLMeP5ROBv2UWC+u3eY2flAg7t/b287MbOrCWa6bwb+x8w+R7BOz1+Z2bEE62fNI1h874Zw+7XAAoIJWQ3AT939ejNrJJgQOQH4phbkkySoWUmy7hl3/9/AicC0/dmBmU0DTnL3DwJPQjCfBfgicG84l2EjwUJ9Hw1/7SSguEz3CoKZ1583szEEy3F8lWBhxm/t118l8hYpOUjW/d/w8VmCNZGKxg1hH+8gWDIDgjWX9sjdNwPbzOx0YF04ERKgLfx+A0EN4miCGdYrCJZVERl2Sg6SdceGj8cQ3PDl4HD9q/cMYR8vE6z3z4Df6yK6XtJy4BaCJTeKZoY1hqkEs3GfAz4BnEKwWqvIsFNykKz7gJk9Afya4IP7CuBygtU6y+LuLwMR6dMvAAAAc0lEQVRPmdmTBE1BRb8FpprZE2b2sfBn9wM9hM1PoTnAE8D1YQ3iMuDfw5h0C1FJhGZIiwwTMzsU+AXwb+5+Y/iz2wg7wZOMTWQgJQcREYlQs5KIiEQoOYiISISSg4iIRCg5iIhIhJKDiIhEKDmIiEjE/wf+uamsjq4F6wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析建筑类型影响\n",
    "var = 'buildtype'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-建筑类型.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEJCAYAAAB/pOvWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHGlJREFUeJzt3X90XOV95/H3SDKWbckxHtpASYqPcfii8LMVZek25AfIIENwWOOkpiEWjRObJeAskF2a3cSuwW6T7BKCk+KihixjCnU5EGwrwQKZ3YRfZiFTiE0RX3BB3aWAYw/Bxr8tafaPe2UkjS2P7Jm517qf1zk6o2fm0cx3dK70mefe+zw3lc/nERER6a8q6gJERCR+FA4iIlJA4SAiIgUUDiIiUkDhICIiBRQOIiJSQOEgIiIFFA4iIlJA4SAiIgUUDiIiUkDhICIiBWqiLuBwZLNZLQglInIYGhsbU8X0OyrDAaCxsTHqEkREjirZbLbovtqtJCIiBRQOIiJSQOEgIiIFFA4iIlJA4ZBwuVyO+fPnk8vloi5FRGJE4ZBwmUyGDRs2sHz58qhLEZEYUTgkWC6Xo729nXw+T3t7u0YPIrKfwiHBMpkMvb29APT09Gj0ICL7KRwSbO3atXR3dwPQ3d1NR0dHxBWJSFwoHBKsqamJmppgknxNTQ1Tp06NuCIRiQuFQ4K1tLRQVRVsAtXV1cyePTviikQkLhQOCZZOp2lubiaVStHc3Ew6nY66JBGJCYVDwk2fPp2xY8dy2WWXRV2KiMSIwiHhVq9ezc6dO2lra4u6FBGJkZIv2W1m1wDnhM2pwH3AKcA6d7/NzM4DbgU2AdcAe4EVQC+wzN0fL3VNcmCD5znMnj1bu5ZEBCjDyMHd/9bdvwJ8H/gxcKK7zwQmm1k9sBi4ArgTuAqYA7SFfVpKXY8cnOY5iMjBlPNiP18HHiEYIQCsB04Cdrn7NjPLAjPCx1rD263FPnlnZ2ep6kysxx57bMA8h0cffZTm5uaIqxKROChLOJjZiUA3sHPQa40H9gxqb+7XZ2Kxr9HQ0HCEVcpFF13EI488Qnd3NzU1NVx88cX6vYqMYHG4EtzlwINAFjg/vK8R6AJ2mVma4LjEG8CzwAVmVgVMKFM9cgCa5yAiB1OucGgGnnb3d4FnzOwh4HV3fwtYAtwFXBfePgKcBqwkOA4hFaJ5DiJyMKl8Ph91DcOWzWbzjY2NUZcxIuRyORYtWsTChQsVDiIjXDabpbGxMVVM33IekJajQDqdZunSpVGXISIxo0lwIiJSQOEgIiIFFA4iIlJA4SAiIgUUDiIiUkDhICIiBRQOIiJSQOEgIiIFFA4iIlJA4SAiIgUUDiIiUkDhICIiBRQOIiJSQOEgIiIFFA4iIlJA4SAiIgUUDiIiUkDhICIiBRQOIiJSQOEgIiIFasrxpGY2GlgFzARmA83Ay+7+F2Z2MnAX8C5wg7v/m5ndDdQDq93978tRk4iIFK9cI4dbgB8B1cAMd58ObDOzU4ElwFzgBuA6M5sGvOnuXwAuLlM9IiIyDCUfOZjZmcA1wGTgbOC58KEsMAUY5+6vh33rgJOBp8M+b5pZnbtvL3VdIiJSvHLsVmoBrnX3+8zsPmBMv9caD+zp13fioHZfn0OGQ2dnZ2mqFRGRAuUIh+3AK+H3O4EJZpYCzgEeA7rMrCF8bDvwLPDF8LFTgc3FvEhDQ0OJyxYRGdmy2WzRfcsRDq3A98wsD/wG+DHwEMFxhXVm9gZwO8GI4kZ3f93MdpvZw8BKd99XhppERGQYUvl8Puoahi2bzeYbGxujLkNE5KiSzWZpbGxMFdNX8xxERKSAwkFERAooHEREpIDCIeFyuRzz588nl8tFXYqIxIjCIeEymQwbNmxg+fLlUZciIjGicEiwXC5He3s7+Xye9vZ2jR5EZD+FQ4JlMhl6e3sB6Onp0ehBRPZTOCTY2rVr6e7uBqC7u5uOjo6IKxKRuFA4JNj5558/ZFtEkkvhkGBH4+x4EakMhUOCPfXUUwPaTz75ZESViEjcKBwSrKmpierqagCqq6uZOnVqxBWJSFwoHBKspaVlfzjU1NQwe/bsiCsSkbhQOCRYOp2mubmZVCpFc3Mz6XQ66pJEJCbKcT0HOYq0tLTQ1dWlUYOIDKBwSLh0Os3SpUujLkNEYka7lUREpIDCQURECigcRESkgMJBREQKKBxERKSAwkFERAqU9FRWMzsL+BHgwK+A3wdOAda5+21mdh5wK7AJuAbYC6wAeoFl7v54KesREZHDU+qRwznAQnf/CvBL4ER3nwlMNrN6YDFwBXAncBUwB2gL+7SUuBYRETlMpZ4Edy7wKTO7Efhb4Onw/vXAScAud99mZllgRvhYa3i7dTgv1NnZWYJyRUTkQEodDje7+3tmNhrYCfzHfq8zHtgzqL25389OHM4LNTQ0HGGpIiLJks1mi+5b6t1KXw5v64B/BvouLdYIdAG7zCxNsPvpDeBZ4AIzqwImlLgWKUIul2P+/PnkcrmoSxGRGCl1OPzGzB4B7gGuBZ4xs4eA1939LWAJcBdwXXj7CHAasJLgOIRUWCaTYcOGDSxfvjzqUkQkRlJH46Uis9lsvrGxMeoyjnq5XI4rr7ySvXv3Mnr0aO6//34t2y0ygmWzWRobG1PF9NU8hwTLZDL09vYC0NPTo9GDiOyncEiwtWvX0t3dDUB3dzcdHR0RVyQicaFwSDBdQ1pEDkbhkGAtLS30HXPK5/O6GpyI7KdwEBGRAgqHBMtkMqRSwYkLqVRKB6RFZL+iw8HMJphZbTmLkcpau3YtPT09QHC2kg5Ii0ifQ4aDmV1tZv9EsE7Sl83s++UvSyrh3HPPHbItIslVzMhhLsFyF79x9zsJFteTEWDjxo1DtkUkuYoJh63AfwBGmdm5wHvlLUkq5c033xyyLRIlrfsVrWLC4WqCBfS2AVcSXINBRoCPfvSjQ7ZFoqR1v6J1yHBw903AEne/BLg/bMsIMHny5AHtk08+OaJKRAbK5XK0t7eTz+dpb2/X6CECxRyQXgZ8xcxSwLVmptVTR4jnn39+QPu5556LqBKRgbTuV/SK2a10prv/tbvn3f3PAV1lZ4Roamoa0NbyGRIXWvcresWEw9tmdr2Z/bGZfQ3Q+G6EmD59+oD2ZZddFlElIgM1NTVRUxNcqLKmpkYfXCJQTDh8Kez3JaA6vJURYPXq1QPabW1tEVUiMlBLSwtVVcG/p+rqaq37FYFDXkPa3XcBd1SgFqmwRx99dEC7vb2dG264IaJqRD6QTqdpbm6mra2N5uZmXYQqAocMBxm5+pbOOFhbJEotLS10dXVp1BCRg4aDmd3t7nPMbAPQdy3RFJB39zMrUp2UVd8Bv4O1RaKUTqdZunRp1GUk1kHDwd3nhLdnVK4cqaRx48axY8eOAW0REdCS3Yl25plnDtkWkeQ65DEHM0u7+7BOXzWzbwBbgFPCr3XufpuZnQfcCmwCrgH2AiuAXmCZuz8+zPrlCGSz2SHbIpJcxYwchvUP28ymEKzHNAY40d1nApPNrB5YDFwB3AlcRbBOU1vYp2U4ryNHbu/evUO2RSS5ijlb6YdmdgfwP4AdAO7+7oE6hktsLAH+Ejie4BoQAOuBk4Bd7r7NzLLAjPCx1vB263AK7+zsHE53KZJ+ryICxYXDVeFt3+ImeeCCg/SdBzxAMIv6WIKzm/peZzywZ1B7c7+fnVhcyYGGBq3iUQ76vYqMXMPZdVzMJLjPDOO1Pwn8BvgIwT//UQQjg0bgYWCXmaWB04E3gJeAC8xsIzBhGK8jIiJlVMwB6YkEV4P7fYJ/6D92998eqK+7/1n4M58GJgFjzOwh4AV3f8vMlgB3EYw+5hHsSvoBsJLgOIRU0AknnMDbb789oC0iAsXtVnoIuBf4JcGKrA9x8N1KALj7L/o1l/W7/xVg5qDu1xdTqJTeli1bhmyLSHIVc7ZSN5Bx93UExx32lbckqRTNkBaRgykmHH4P+Bczawc2AieY2epD/IwcBfL5/JBtEUmuYnYrXVL2KiQSqVRqQCCkUqkheotIkhRzttK/VqIQERGJj6FWZZ1xsMfc/aflKUcqSbuVRORghjrmcMZBvk6vQF1SAdXV1UO2RaKUy+WYP38+uZyuTByFoZbsXlTJQqTyamtrByzZXVtbG2E1IgNlMhk2bNjA8uXLdYXCCGjJ7gTrHwwHaotEJZfL0d7eTj6fp729XaOHCBQzQ3oq8FWg72Pl8e5+blmrkooYP34827ZtG9AWiYNMJkNvby8QXL5Wo4fKK2bksAS4D3gf+BvgtbJWJBXTPxgO1BaJytq1a/dPyuzu7qajoyPiipKnmHBIESydkXb3R4HJ5S1JRJKuqamJmppgx0ZNTQ1Tp06NuKLkKSYcFgGfBR42s/8HvFzekkQk6VpaWqiqCv49VVdXM3v27IgrSp5iJsH9rF/zrjLWIiICQDqdprm5mba2Npqbm0mn01GXlDiHHDmY2U2D2o+UrxwRkUBLSwtnnHGGRg0RGWqGdC0wDrjczH5CcOxhHMHlP0VEyiqdTrN06dKoy0isoXYr/SlwNXAawTUcUsBO4JbylyUiIlEaaoZ0BsiY2f929yEv7iNHp9raWnbv3j2gLSICxS3ZfZGZTQJOBdzd3yhvSVIp+/btG7ItIslVzKms1xOcpfQp4E4zu7G8JUml9PT0DNkWkeQqZuQww90/0dcws6eA75evJBERiVox4bDNzKYDzwPnAFpjQURkhCsmHGYD3wSuATqBBUN1NrOxwA8JFupbA3wcOAVY5+63mdl5wK3ApvA59wIrgF5gmbs/fnhvRURESqWYYw63u/tN7n6Ju98E3HGI/ucCS939i0ALcKK7zwQmm1k9sBi4ArgTuAqYA7SFfVoO943I8A2edXrcccdFVImIxM1Qk+BOJZjjcHa/S4aOBUYN9YTu/gszS5vZ7cAqgpEBwHrgJGCXu28zsyzQ97yt4e3Ww3sbcjgGr5G/ZcuWiCoRkbgZarfShwkuCVoX3qaAXcCVRTzvduBp4LvhV99rjQf2DGpv7vdzE4stvLOzs9iuMgz6vYoIDD0J7pfAL83sd9296FnRZnYFkHX3B81sHnA+wcigEXgY2GVmaYLAeQN4CbjAzDYCE4p9nYaGhmK7yjDo9yoycmWz2aL7FrMq69eG+fobCOZD7AJ+Akwws4eAF9z9LTNbQjBvIg/MI9iV9ANgJcFxCBERiVgxZysNi7u/Clwy6O5l/R5/BZg56PHrS12HiBzdcrkcixYtYuHChVqyOwLFnK0kIlJxra2trF+/ntbW1kN3lpJTOIhI7ORyuf3Xje7o6Cg4s07KT+EgIrHT2tpKb28vAL29vRo9REDhICKx8/jjjw/ZlvJTOIhI7OTz+SHbUn4KBxGJnQsvvHBAu6mpKaJKkkvhICKxM2/ePKqqgn9PVVVVzJ07N+KKkkfhICKxk06n948Wpk6dqnkOESj5JDgRkVKYN28e77zzjkYNEdHIQURECigcRCSWMpkMGzZsYPny5VGXkkgKBxGJnVwuR3t7O/l8njVr1miGdAQUDiISO5lMhn379gGwb98+jR4ioHAQkdjp6OjYP/Etn8/z2GOPRVxR8igcRCR2PvzhDw/ZlvJTOIhI7GzatGnItpSfwkFEYmfq1KmkUikAUqkUF110UcQVJY/CQURip6WlhZqaYI7uqFGjmD17dsQVJY/CQURiJ51OM23aNFKpFNOmTdPyGRHQ8hkiEkstLS10dXVp1BARhYOIxFI6nWbp0qVRl5FY2q0kIrG0ceNGLr30UjZu3Bh1KYlU8pGDmf0hcAdB8HwfaAROAda5+21mdh5wK7AJuAbYC6wAeoFl7q7rAYoIixcvZseOHSxevJh77rkn6nISpxwjhz8APgdcSvDP/0R3nwlMNrN6YDFwBXAncBUwB2gL+7SUoR4ROcps3LiRrq4uALq6ujR6iEDJRw7ufjeAmV0GPAm8Ez60HjgJ2OXu28wsC8wIH2sNb7cW+zqdnZ2lKVgG0O9V4uCWW24Z0P72t7/NggULIqommcpyQNrM0sDXgR8CffPea4DxwJ5B7c39fnRisa/R0NBw5IVKAf1eJQ7efvvtgra2zSOXzWaL7lvy3UpmVgv8T4JweBI4P3yoEegCdoXhcQ7wBvAscIGZVQETSl2PiBx9Jk2aNGRbyq8cxxy+SbD76Ibw6xkzewh43d3fApYAdwHXhbePAKcBKwmOQ4hIwn3rW98asi3lV45jDguBhYPuXtbv8VeAmYMev77UdYjI0WvKlClMmjSJrq4uJk2axJQpU6IuKXE0z0FEYum6666jqqqK66/XZ8coKBxEJJaeeOIJ8vk8TzzxRNSlJJLCQURip/81pNvb23UN6QgoHEQkdjKZDD09PQB0d3frGtIRUDiISOysXbt2fzj09PTQ0dERcUXJo3AQkdg566yzBrTPPvvsiCpJLoWDiMTOr3/96wHtF198MaJKkkvhICKxs3PnziHbUn4KBxGJnbq6uiHbUn4KBxGJnRtvvHFA+6abboqokuRSOIhI7KxcuXJA++GHH46okuRSOIhI7Kxfv37ItpSfwkFERAooHEREpIDCQURECpTlMqFSnPfff589e/YcumMFbdmyJZLXHT16NPX19ZG8togUUjhEpLu7m1mzZrFjx46oSxlg5szB12GqjHHjxrFq1SpqarRJisSB/hIjUlNTw4oVKyIdORwoCB588MEIKglGDgoGkfjQX2OE6uvrI92VMm7cuAEjl/r6eo477rjI6hGR+NAB6QT7+c9/PqDd1tYWUSUiEjcKh4QbM2YMEIwiRET6lGW3kpldDMx096+a2V8BpwDr3P02MzsPuBXYBFwD7AVWAL3AMnd/vBw1yYHde++9zJw5k0wmE3UpIhIjJR85mNnHgUuAUWbWAJzo7jOByWZWDywGrgDuBK4C5gBtYZ+WUtcjIiLDV/JwcPeXgdvD5snA0+H364GTgF3uvg3IAlMG9dla6npERGT4yn22Uv/zNGuA8f3u62tv7tdnYrFP3NnZecTFCbz33nsAvPbaa2zevPkQvUWio7/5yip3OGSB2UAr0Ag8DOwyszRwOvAG8BJwgZltBCYU+8QNDQ2lrzaB+mZEf+xjH9NprBJr+ps/ctlstui+ZQ0Hd3/XzJ4xs4eAF9z9LTNbAtwF5IF5BLuSfgCsJDgOISIxELflXaJa2gWSubxLWcLB3buAq8PvlwHL+j32CjB4au715ahDRA5PHJd3iWppF0jm8i7JeaciUrSol3e5++67WbNmzf72Zz/7Wa6++upIaoFkLu+SrHcrIkWLcnmXm2++eUA4fOMb34ikjiTTDGkRiaVp06YBwahBKi+xI4ft27eze/fuqMuI3LvvvjvgNulqa2upq6uLugwB5syZw5o1ayLdnZRkiQyH7du384VZV7Jz+/tRlxIbc+fOjbqEWBhbV88DK/5BASGJl8hw2L17Nzu3v8+Oj08nP2ps1OVEL98LKe1hTO3bCS+vZvfu3QoHSbxEhkOf/Kix5I9ROIiIDJbocBCJIx0PC+h42ECVPh6mcBCJke3bt/Nns77Atu07oy4lNnQ8LDC+biz3r3igYgGR6HBI7dsVdQkSI3HYHnbv3s227Tu55Y/e40PH9EZdTuR68lCdirqK6G3dW8WC56no8bBEhkNPTw8A415eFXElEkd920eUPnRML8eOzkddhsRG5T8oJDIcqqurAdjx8c+RHzUm4mokLlL7djHu5VX7t48obd2jj8vygSi2h0SGQ5/8qDE6W0lipW/UsuBXx0ZcicRRJUe1iQ4HkbjpG7Xccs5v+ZB2K0lo654UC351bEVHtYkOh9Q+nRECaBJcKFbbQypFcMmTZNMB6VBKu5Uqora2lrF19fDy6qhLkZgZW1dPbW1tZK9fW1vL+LqxLHg+shIkpsbXja3otpnIcKirq+OBFf+giUYEE4zmzp1La2srEycWfQnvESvqhffq6uq4f8UD2jbRtjmYJsFVSF1dndbP6WfixIm6hnRMaNscSNtmNLSjWURECigcRESkgMJBREQKxOKYg5lNBO4HtgOL3H1DxCVVxPvvvx/ZBdz7xGXly9GjR0d2vWI5sKi3z7hsm5DM7TMW4QDcDHwXeAb4EfDVaMspv+7ubmbNmsWOHTuiLgWIfuXLcePGsWrVKmpq4rJJJlucts+ot01I5vYZl3d6MvC0u+81s0Scw1dTU8OKFSsiHzlA8I8g6o1+9OjRkdcgH4jL9hmHbROSuX3G5d3uAfqmABZ1QnM2my1fNSIiCReXcHgW+IyZPQWMPlTnxsZGTagXESmjuJyt9BNgFsFB6e9GXIuISOKl8nkt7iUiIgPFZeQgIiIxonAQEZECCgcRESmgcBARkQIKhxHMzI43szNL8DwpM9PpwxJL2j7LQ2crjTBmNgb4I6AXuAQ4BlgZPlwFvOTu74Z9/w5Y6O5vHeB5bnf3G8zsLODfA9XAq+7+WAXehoxgZjYHmMkH10HtJZhz9T13/1/9+mn7jJDCYYQxs/HADKAHWAgs4oM/wqrw/mvC21OALmAv8J67Xx4+RzVwJ3AtsAA4FjgLeBH4ibv/ukJvR0YwM5sBjHH3+/rdNw34r2j7jJx2K40w7r7N3e8BJgD1wBzgK+HXKIJPaf/o7p8GVgMzwu8BMLNjgHXA54AbgVuA+4AMkKXI5U1EilBDsHROfxPQ9hkLGjmMQGZ2KbAC+CLwXHj3acCfAK8CtwL/xsBPZse5++n9fv4GghHGYuCfgY8ATwKPu/vblXovMrKY2UnA3UA38FGCbS8FvAPUAj8mGPFq+4yYRg4jjJldDFwN3AFcSLAsySygmWDUkALuOMAns3/p9zRNwGbgPwO/Q7CE+qeAeQSBI3JY3P1f3b3J3ZuBp4EvAyvdvTncDnvQ9hkLCoeRp4MgDHYDnwQuD78+Ez6eAr5uZr8ApgM/Db/fB/svvPQLIAd8jeCYxQvAd4DPE3y6EzkiZvYRgl1Avx30kLbPmFA4jDDu3uvuPQR/ZNe7+6fDT15fJTgw/Svg8n6fzL4Q9plpZn8O5N19Vfh0fwL8J+C/h8/3X4A3K/qGZMQxs+OBfwT+gmA0Oz68/78RbF/aPmMgLkt2S+ntX/rczCYQHLS7yd1f7dfnRWClmb1D8EFhArA8fKwO+GPgS8AfEGwrf+3umytQu4xQZnY6wXGF69x9Yzg/IW1ma4EdBLuUtofdtX1GSAekRaSizOwYd98bdR0yNIWDiIgU0DEHEREpoHAQEZECCgeRAzCzSWb2sxI+1yWleC6RSlE4iJTfJIJFEEWOGjqVVRLPzMYB9wK/Czzm7rccoE8DsAwYCyxw93Yze8LdPxmu9/Nzd59qZn8JTCOY6fun4ffXAr8Tnsb5eWALcBdwKvB/gavdvdvMXgJ+CjS6+6XlfdciQ9PIQQTmAuvc/RPABWZ23AH6fIdguYYLCdb+AXjBzE4FPgF0hOfs/5bg/Pt1wHR3byWYqPVwOJlrM3AZsMvdPwk4cFX4fFOA1xQMEgcaOYgEC7z9u3BBtzTwe8C2QX0+RjATF2BceLsSmAqcSLCYXCp8rp8R/G0dbAG4Bj5YEPF54ILw+7fd/d4jeiciJaKRg0iwUu33wiUbvkOwbs+B+lxJsEZV3yzdJ4A/BD7i7q+F35/o7pcA/9TvZ3cR7I7q0wmcE37fCLwcfr/jiN+JSIkoHESgFZhhZk8SHDjedIA+3yQ4LvF/CEYIhGtY7QFeD/u8ChxvZk8BZwDHh/e/AJxgZk+b2eeBNmCcmT1BMIr4+7K8K5EjoBnSIiJSQCMHEREpoHAQEZECCgcRESmgcBARkQIKBxERKaBwEBGRAgoHEREp8P8BUicLGqikzEcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析电梯影响\n",
    "var = 'elevator'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-电梯.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析楼高影响\n",
    "var = 'height'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-高度.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析年份影响\n",
    "house.plot(x=\"year\", y=\"totalprice\", kind=\"scatter\")\n",
    "plt.savefig('总价-年份.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析城市影响\n",
    "var = 'city'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-区域.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析面积影响\n",
    "house.plot(x=\"size\", y=\"totalprice\", kind=\"scatter\")\n",
    "plt.savefig('总价-面积.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析房间影响\n",
    "var = 'main_room'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-房间数.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAEICAYAAAC0+DhzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHz1JREFUeJzt3X903HWd7/Fn2samTVJKGqFeLguL0DdBrUC8tce7RUVTCLu43rbWXUTGXeoWvRarcs6uHn60/Nh73SOi1ksNt7o7ruzBlipLr402cACRtodlRCIS3hShd0Vc2mlvfyRt2qTJ/eM7KUkmSWcm35nv/Hg9zsmZfDKfme97Jj/e+fyuGhwcREREZLgpUQcgIiLFR8lBRETSKDmIiEgaJQcREUmj5CAiImmUHEREJI2Sg4iIpFFyEBGRNEoOIiKSRslBRETSTIs6gFwkEgnt+SEikoPm5uaqTOqVZHIAaG5ujjoEEZGSkkgkMq6rbiUREUmj5CAiImmUHEREJI2Sg4iIpFFyEJHQJZNJVq1axb59+6IORXKk5CAioYvH43R2dhKPx6MORXKk5CAioUomk7S3tzM4OEh7e7taDyVKyUFEQhWPxxk6m35gYECthxKl5CAioero6KCvrw+Avr4+tm3bFnFEkgslBxEJVUtLC9XV1QBUV1ezePHiiCOSXCg5iEioYrEYVVXB9j1TpkwhFotFHJHkQslBRELV2NhIa2srVVVVtLa2MmfOnKhDkhyU7MZ7IlK8YrEYu3fvVquhhKnlICIiaZQcRCR0WgRX+kLvVjKzG4D3pIotwP3APGCHu99tZguBO4A3gBuA48ADwACw3t0fDTsmESmc0YvgYrGYxh1KUOgtB3f/jruvAL4ObADOcvdlwHlmVg/cCSwF7gWuBa4HtqTqqINSpMRpEVx5yOeA9OeBrQQtBIBO4BzgqLsfMrMEsCR1332p24OZPnlXV1dYcYpIiH72s5+NWAT305/+lNbW1oijkmzlJTmY2VlAP3Bk1LVmAcdGlfcOq9OQ6TWampomGaWI5MMVV1zB1q1b6evro7q6miuvvFK/r0WiGI4J/SjwIJAAFqW+1gzsBo6a2RyCcYlXgZ3A5WY2BZidp3hEpEC0CK485Cs5XAk85e77ge1mthl4xd1fB+4C2oDPpW63Au8AHiIYhxCREqZFcOWhamjgqJQkEonB5ubmqMMQkXEkk0nWrl3LmjVrlByKSCKRoLm5uSqTulohLSKha2xsZN26dVGHIZOgRXAiIpJGyUFERNIoOYiISBolBxERSaPkICIiaZQcREQkjZKDiIikUXIQEZE0Sg4iIpJGyUFERNIoOYiISBolBxERSaPkICIiaZQcREQkjZKDiIikUXIQEZE0Sg4iIpJGyUFERNIoOYiISBolBxERSTMtH09qZtOBfwWWAdcBVwIvuPvfmdnbgTZgP/AFd/+9mX0XqAcedvcf5CMmERHJXL5aDrcD3wamAkvc/SPAITO7ELgL+BvgC8DnzKwVeM3dlwNX5CkeERHJQugtBzObD9wAnAdcDDyduisBnA/Uuvsrqbp1wNuBp1J1XjOzOnfvPtV1urq6wg5dRERS8tGtFAM+6+73m9n9wIxh15oFHBtWt2FUeajOKZNDU1NTONGKiFSIRCKRcd18dCt1Ay+mPj8CTDezKuA9wKvAbjNrMrNzUnV3Apen6l8I7M1DTCIikoV8tBzuA/7BzAaBPcAGYDPBuMIOM3sVuIegRfFFd3/FzHrN7MfAQ+7el4eYREQkC1WDg4NRx5C1RCIx2NzcHHUYIiIlJZFI0NzcXJVJXa1zEBGRNEoOIiKSRslBRETSKDmIhCiZTLJq1Sr27dsXdSgik6LkIBKieDxOZ2cn8Xg86lBEJkXJQSQkyWSS9vZ2BgcHaW9vV+tBSpqSg0hI4vE4Q1PDBwYGKrr1oO610qfkIBKSjo4O+vqCNZx9fX1s27Yt4oiio+610qfkIBKSlpYWqqurAaiurmbx4sURRxQNda+VByUHkZDEYjGqqoLFp1OmTCEWi0UcUTTUvVYelBxEQtLY2EhraytVVVW0trYyZ86cqEOKhLrXyoOSg0iIYrEY8+fPr9hWA6h7rVwoOYiEqLGxkXXr1lVsqwHUvVYulBxEJFTqXisP+TjPQUQqXCwWY/fu3Wo1lDAlBxEJ3VD3mpQudSuJiEgaJQcREUmj5CAiImmUHEREJI2Sg4iIpFFyEBGRNKFOZTWzdwPfBhx4BvgjYB6ww93vNrOFwB3AG8ANwHHgAWAAWO/uj4YZj4iI5CbslsN7gNvcfQXwBHCWuy8DzjOzeuBOYClwL3AtcD2wJVVHq2VERIpE2IvgFgDvN7MvAt8Bnkp9vRM4Bzjq7ofMLAEsSd13X+r2YDYX6urqCiFcEREZS9jJ4W/d/YCZTQeOAJ8Zdp1ZwLFR5b3DHtuQzYWampomGaqI5EsymWTt2rWsWbNGeysVkUQikXHdsLuV/jp1Wwf8BliUKjcDu4GjZjaHoPvpVWAncLmZTQFmhxyLiEREx4SWvrCTwx4z2wr8E/BZYLuZbQZecffXgbuANuBzqdutwDuAhwjGIUSkxOmY0PIQareSu/8A+MGwL/0CWD/s/heBZaMetirMGEQkWmMdE/rFL34x4qgkW1rnICKh0jGh5UHJQURCpWNCy4OSg0iIkskkq1atquh+dh0TWh6UHERCpFk6Oia0XCg5iIREs3TeFIvFmD9/vloNJUzJQSQk8XicEydOANDf31/xrYd169ap1VDCMk4OZjbbzGryGYxIKevo6DiZHE6cOKFZOlLSTpkczOxTZvZLgn2S/trMvp7/sERKz4IFC0aU3/ve90YUSfQ0MF/6Mmk5/A3Bdhd73P1egs31RGSUl19+eUR5165dEUUSvba2Np577jna2tqiDkVylElyOAj8N6DazBYAB/Ibkkhpeu211yYsV4pkMklHRwcA27ZtU+uhRGWSHD5FsIHeIeAvCc5gEJFRzj333AnLlaKtrY2BgQEg2D5DrYfSdMrk4O5vAHe5+1XAv6TKIjLKzTffPKJ86623RhRJtB555JER5aFWhJSWTAak1wMrzKwK+KyZafdUkTHMmzePs88+G4Czzz6b888/P+KIojG0Onq8spSGTLqV5rv7/3D3QXf/K0Cn7IiM44wzzgDgzDPPjDiS6HzoQx8aUf7whz8cUSQyGZls2f0HM1sFPANcCmh0SWQMyWTy5ElbzzzzDPv27avIRWArV65k27ZtDA4OUlVVxcqVK6MOSXKQScvhk6l6nwSmpm5FZJRvfetbE5YryVBXkrqUStcpWw7ufhT4ZgFiESlpTzzxxIjy448/Hk0gEYvH4yOSgw77KU3aW0kkJEOnn41XrhTaRqQ8jJsczOy7qdtfm1ln6uPXZtZZuPBESsfQTKXxypVi0aJFI8qXXXZZRJHIZIybHNz9+tTtu9x9furjXe4+v3DhSSl46aWXaG1tTds+otLcdtttI8pr166NKBKRyVO3kkzamjVr6OnpqdhFX0PmzZvH1KlTAZg6dWrFrnN48sknR5R//vOfRxSJTMYpB6TNbI67ZzV91cxuApLAvNTHDne/28wWAncAbwA3AMeBB4ABYL27P5pl/BKxl1566eQeQq+99hovv/xyxf5RfOmll0b0tVfqe9HS0sJPfvIT+vv7mTZtms6QLlGZtByy+oNtZucT7Mc0AzjL3ZcB55lZPXAnsBS4F7iWYJ+mLak6OjKqBK1Zs2ZEuZJbD3ovArFYjClTgj8tU6dO1WlwJSqTRXDrzOybwNeAHgB33z9WxdQWG3cBa4C5BGdAAHQC5wBH3f2QmSWAJan77kvdHswm8K6urmyqS56MtRNppX5v9F68aeHChTz55JMsXLiQPXv2sGfPnqhDkixlkhyuTd1+P3U7CFw+Tt2VwEaCVdSnA0MrYKYBs4Bjo8p7hz22IbOQA01N2sWjGFRVVY2YsllVVaXvzTCV+l6sXr2agwcPsnr16opcJV6shlbwZyKTRXAfzOLalwF7gP9M8Me/mqBl0Az8GDhqZnOAdwKvAs8Dl5vZy8DsLK4jReL973//iMVeH/jAByKLRYrH0BnSUroyGZBuIDgN7o8I/qBvcPf/N1Zdd78m9ZgPAOcCM8xsM/Csu79uZncBbQStj5UEXUnfAB4iGIeQEnPjjTeOSA433nhjdMFEbMqUKSfPMRgqi5SqTLqVNgP/DDxBsCPrZsbvVgLA3R8fVlw/7OsvAstGVV+VSaBSvIa6lip9H51LLrlkRLP90ksvjTAakcnJ5F+bfiDu7jsIxh368huSlJJ4PD5ibn88Ho84oui88MILI8q/+c1vIopEZPIySQ7/Cfitmf0UeBl4m5k9nN+wpFR0dHTQ398PQH9/f0Xvo3P06NEJyyKlJJNupavyHoWUrJaWFrZu3UpfXx/V1dVa8CRSJjKZrfR/CxGIlKZYLEZ7ezsQDMBW8oKnGTNmjGgtzJgxI8JoRCZn3ORgZkvGu8/df5SfcKTUNDY20traysMPP0xra2tFz2lXt5KUk4nGHN41zsc7CxCXlJCrr76amTNn8pGPfCTqUCJVW1s7YVmklIzbcnB37TcsGdmyZQtHjhzh4YcfrugTv9RykHKiVToyKclkkvb2dgYHB2lvb2ffvqw28C0rwxfAjVUWKSWZrJBuAT4N1KS+NNfdF+Q1KikZ8Xj85N5KAwMDOi+4TB0+fJhjx46duuIwQ1t2Z2v69OnU19dn/TgJVybfubtSH8sJFsFdl9eIpKR0dHTQ1xesi+zr62Pbtm1KDmWmv7+f5cuX09PTU5Dr1dbWsmXLlpwSi4Qnk3e/imDrjM+4+8/MbE1+Q5JSonUO5W/atGls3Lgxq5bD/v37WbFiBRs2bKChIasNl5k+fboSQxHI5DuwFvgz4Mdm9jugcpfAShqtc6gM9fX1OXX1NDQ00NjYmIeIJN8yWQT3f4YV2/IYi5QgrXMQKU+nnK1kZl8aVd6av3CkFMViMebPn1/xrYahDQjHK4uUkolWSNcAtcBHzex7BGMPtQTHf4rIKKeffjrJZPJkOdu+dpFiMlHL4ePAg8A7CM5w2Ax8B7i9AHFJCYnH43R2dlb0dt3AiMQAsHfv3nFqihS/iVZIx4G4mT3m7hMe7iOVa/QiuFgsVrHjDmeccQZ79uw5WT7zzDMjjEZkcjJZIb3YzM41syvN7I/zHpGUlLEWwVWqgwcPTlgWKSWZJIdVBLOU3g/ca2Za4SQnjbUIrlKNXgfQ29sbUSQik5fJOocl7v4nQwUz+wXw9fyFJKVEi+BEylMmLYdDZvYRM3ubmV0NHMp3UFI6YrEYVVVVgBbBjR5j0JiDlLJMWg7XAV8GbgC6gFsnqmxmM4F1BBv1tQMXAfOAHe5+t5ktBO4A3kg953HgAWAAWO/uj+b2UiQKWgT3posuuog33njjZHn+/PkRRiMyOZm0HO5x9y+5+1Xu/iXgm6eovwD4lrt/AogBZ7n7MuA8M6sH7gSWAvcC1wLXA1tSdSr3384SpsN+Ao899tiIckdHR0SRiEzeRIvgLiRY43DxsCNDZwLVEz2huz9uZnPM7B7gXwlaBgCdwDnAUXc/ZGYJYOh570vdZjy9o6urK9Oqkmf3338/R44cIR6Pc80110QdTlGp1J/TAwcOALBr1y6t9yhRE3UrnUlwJGhd6rYKOAr8ZQbP2w08BXw19TF0rVnAsVHl4T85GS8pbWpqyrSq5FEymWTnzp0MDg6yc+dOVq9eXdFdS6NV6s/p0ILACy64QBvvFZFEIpFx3XG7ldz9idRRoVvd/XZ3X+vu/+Duv53oCc1sKfA2d38QeAVYlLqrGdgNHDWzOcB7gFeBncDlZjYFmJ1x5FIU4vE4/f39QDCVtZLXOYxOim9961sjikRk8k455uDu/z3L5/w1wXqIzcD3gO2pz19x99cJDg5qAz6Xut1K0H31EME4hJSQjo6Ok8dhDgwMVPQ6h6GulCH79++PKBKRyQv9RA13fwm4atSX1w+7/0Vg2aj7V4UdhxTGu9/9bnbs2HGyfPHFF0cYTbROnDgxYVmklGQyW0lkXJ2dnSPKzz33XESRiEiYlBxkUkafK1yoc4ZFJL+UHGRSamtrJyyLSGlScpBJufDCC0eUK3Xqpki5UXKQSfnVr341ovzss89GFImIhEnJQURE0ig5iIhIGiUHmRTN7RcpT0oOIiKSRslBJmX0gTZz586NKBIRCZOSQ46SySSrVq1i3759UYcSqUOHRh4MePBgxruui0gRU3LIUTwep7Ozs6J3IYX0vZQuueSSiCIRkTApOeQgmUzS3t7O4OAg7e3tFd16GL2u4Ze//GVEkYhImJQcchCPxxkcHASCbaorufXQ29s7YVlESpOSQw46Ojro6+sDggNuKvkMAxEpT0oOOWhpaaG6OjhKu7q6msWLF0cckYhIuJQcchCLxaiqqgJgypQpxGKxiCMSEQmXkkMOGhsbaW1tpaqqitbW1rSzg0VESl3ox4RWilgsxu7du9VqEJGypOSQo8bGRtatWxd1GCIieaFuJRERSaPkICIiaULvVjKzS4FvEiSerwPNwDxgh7vfbWYLgTuAN4AbgOPAA8AAsN7dHw07JhERyU4+Wg6XAH8O/CnBH/+z3H0ZcJ6Z1QN3AkuBe4FrgeuBLak6Gt0VESkCobcc3P27AGZ2NfAk8B+puzqBc4Cj7n7IzBLAktR996VuM97Ss6urK5yAJXT63rypUt+LAwcOALBr1y727t0bcTSSi7zMVjKzOcDngXXA0Ib/04BZwLFR5eE/OQ2ZXqOpqWnygUpe6HvzpmJ8L7q7u/O+B9bUqVMBmDNnDg0NGf9a56ympoa6urq8X6fUJRKJjOvmY8yhBvhHguTwB4Lxh/sIxh5+DBxNJY93Aq8CzwOXm9nLwOyw4xGRN3V3d/Pxj32Mwz09BbneihUrCnKd+tpafrhpkxJEiPLRcvgyQffRFwiSw3Yz2ww86+6vm9ldQBswCKwk6Er6BvAQwTiEiORJb28vh3t6+AyQ7z+jAxRmOmQ3sL6nh97eXiWHEOVjzOE24LZRX14/7P4XgWWj7l8VdhwiMr46YBZVUYcRksGoAyhLWucgIiJplBxERCSNkoOIiKTRxnsiIlk4fPgwx44dO3XFYfr7+5k2Lfs/t9OnT6e+vj7rx4VByUFEJEP9/f0sX76cngJNBa6trWXLli05JZbJUnIQEcnQtGnT2LhxY1Yth/3797NixQo2bNiQ9YLA6dOnR5IYQMlBRCQr9fX1OXX1NDQ00NjYmIeI8kMD0iIikkbJQURE0ig5iIhIGo05iIwjlymLoyWTyYzqRTllUWQsSg4iYwhryuKSJUtOXYlopyyKjEU/iSJjyGXK4liJ4Ec/+lFGj41yyqLIWPTTKDKObKcsfu1rX+Omm246Wb7nnntKauqiyHBKDiIhWbBgwYhyc3NzRJGcWjdQLltdd0cdQJlSchAJ0a233srtt9/OmjVrog5lTCdOnACGHbBSRoZem4RDyUEkRBdffDEA8+fPjziSsQ2d7VyIk+AKpZsg2Q29tqweW4DztPfv3z/iNt/COk9byUGkAukkuCAxLF/+cbq7D4ccz9gKdZ52XV09Gzf+cNIJQslBRCpSb28v3d2HabnoOmqqa/N6rYHBAaZU5X/NcW9fDx0vfD+U87SVHESkotVU1zLjLeXSyRYebZ8hIiJp8tJyMLMrgGXu/mkz+3tgHrDD3e82s4XAHcAbwA3AceABYABY7+6P5iMmERHJXOgtBzO7CLgKqDazJuAsd18GnGdm9cCdwFLgXuBa4HpgS6pOLOx4REQke6G3HNz9BTO7B1gDvB14KnVXJ3AOcNTdD5lZAhjab+C+1O3BTK/T1dUVTsASukr+3hw4cACAXbt2sXfv3oijSTcUXznK9j3XezGxfA9ID9+YZhowa9jXhsrDX0HGZ+g1NTVNOjjJj0r+3gztwnrBBRcU5dYZme4SW4qyfc8r8b1IJBIZP0e+k0MCuI6gZdAM/Bg4amZzgHcCrwLPA5eb2cvA7DzHI6egbapFBPKcHNx9v5ltN7PNwLPu/rqZ3QW0EaxcWUnQlfQN4CGCcQiJiLapFpEhefmtdPfdwKdSn69n2FYu7v4isGzUQ1blIw7JTjlvU12IbRKgsFslhLVNgshY9C9bjpLJJGvXrmXNmjXMmTMn6nBCk+021R/84Ad57LHHTpZbWlqKrq+9u7ubjy3/GD3dk2sRZaMQWyXU1tWyaeMmJQjJCyWHHLW1tfHcc8/R1tbGV77ylajDiczatWtHJIdbbrklwmjG1tvbS093Dyc+fAJqCnDBAfK/vLQXeh7pyXmbhEJs2V2ItwEmv2X3gSN76e3L/B+HvhPHOTHQP8mrZmbqlGlUT31LxvV7+46Edm0lhxwkk0k6OjoA2LZtGytXriyr1kO23ve+97F9+3Yuu+yyqEOZWA0wI+ogolVTU0N9bS3rJzmuVGzqa2upqcku81dXVwPw5K4H8xFSpIZe22QoOeSgra2NgYEBAAYGBiq+9XDTTTexZMkSVq9eHXUocgp1dXX8cNOmgmxTvWLFCjZs2EBDQ8Yz1HOWy/jLaaedxsaNGzl06FBWjzty5AjHjx/P6jG5estb3sLMmTOzesysWbM47bTTJn1tJYccPProyB0+HnnkkYpODlJa6urqCjZO0dDQUHRjUMPNnTuXuXPnRh1GUdLGezkYHBycsCwiUurUcsjBokWLePzxx0+Wi76vXQL5n8laOOX0WqQoKTnk4PDhwxOWpbgMnS089ZHsj5Esdjo3WfJFySEHo/cneeaZZyKKZGJa+BUYOlu4YFNZC6E3SHa5nJsskgklhzLV3d3NXyxfxqHu8OY9n0ohFn7NqpvJAxsfzG1AVVNZRTKm5FCment7OdR9hDsWHGL29IG8X+/EIEzN83n1B45N4Zanyf183EL10xdoEZxIPik5lLnZ0wc4fXq5zKbKLcnV1NRQW1dLzyPltfCrti77hV8imVJyKHMHj+X53/kCyvW11NXVsWlj/hd+QWEXf2njPcknJYcyNTSL5eanJ79SstjkMkOnkAu/oPgXf4mcipID5XnAzdAsljsXHOS0MulWOnisipufPk0zdCKQ7e/IZGaw6RCo4lDxyaHcD7gZpIp8774JhRmQDl6LFNpkfkdymcGmQ6CKQ8W/+7kccLN582buv//+k+XrrruOj370oxk9tlAH3NTU1DCrbia3PJ33SxXUrLqZGoQtsFx+RyBIKrn8rBfyECgZX9l9Bwqx8Gvp0qUjkkOmiQHg2LFjWf+S5TLwWFdXxwMbH9QgrIQi20OgpPSVVXLo7u5m2ceWc6Rnssd/ZCfTLqVczayt48FNG3NKEBqEFZFclNWurD09PQVPDIVwpKd70mMiIiLZKKuWQ21tLTNr68ouQcysraO2trYg18pl5lauM1OKfVaK3gupZFXFcBaBmTUA/0JwHOxad//1RPUTicRgc3PzmPdlO+Zw4sQJYrEYR44UZg+imTNnEo/Hs5qOWah+9v7+fq6++uqCtVKKeVaK3gspR4lEgubm5oym/RXLT+LfAl8FtgPfBj6d6xPl0s++adOmnNY55DIbo5j/Q8x1Vgrk/l4U6x9DvRdS6Yrlp/HtwFPuftzMCr6lmGZivEnvxZv0XkglK5bkcAxOrnDKaC7k6DMVREQkPMWSHHYCHzSzXwDTT1U50z4zERHJTbFMZf0e8BcEg9JfjTgWEZGKVxSzlUREpLgUS8tBRESKiJKDiIikUXIQEZE0Sg4iIpJGyUFERNIUyzqHkpLtXlDlzsyuAJa5e87bnpQDM7sU+CbBP11fd/fNEYcUGTObCawDaoB2d/9BxCFFysxuApLu/k9Rx5IptRxyM7QX1CeAGyOOJVJmdhFwFVAddSxF4BLgz4E/BT4VbSiRWwB8y90/AcSiDiZKZnY+JfjzoOSQm6G9oI4BBd8Lqpi4+wvAPVHHUQzc/bvuvh9YBPxb1PFEyd0fB14zs3uAH0YcTmTMrAq4C1gTcShZU3LITdZ7QUllMLM5wOeBu6OOpQh0A08B/yXqQCK0EtgIJKMOJFtKDrkZ2guqjgz2gpLKYGY1wD8Cn3f3ij66z8yWAm9z9weB86KOJ0KXEbQkPwdcY2ZnRxxPxjQgnZvvAf8L+CxwR8SxSPH4MnAO8AUz+4O73xJ1QBH6NXCvmR0l+H2pSO5+DYCZfQA4191/F21EmdPeSiIikkbdSiIikkbJQURE0ig5iIhIGiUHERFJo+QgIiJplBxEMmRmz0cdg0ihKDmIiEgarXMQ4eQuoj8CaoEXgQsIdppNmtnz7v5OM/sd8CxwJnCNu/92nOd6Hvg2sNLdLzGzGcD3gbnAM+7+BTN7a+prp6Wu+yCwDTgMdBFsXLfY3Xfn7UWLTEAtB5HARcAhd18EPMrYvxuNwF8BNwM3TfBcNcBsd78kVf40sD313LNSq2W/DNwH/Fdgcep6PQS7/R4FNgPvmORrEsmZkoNI4JfAv5tZB0GrYWDYfTWp29+5+z7gN8AfT/Bc/YzcqbYJeDr1+b8RJKIm4Gl3H0xd+0rg96nr/h44wZubO4oUnJKDSOBioMPdW4APAYPA6anzKs5I1TnLzGYDBvz7BM/Vn9rOfUgX8J7U583AC6O+djHws1BehUhIlBxEAq8CN5nZDuA/gB8QnGT2SeAPqToHgPsJun6+lsVz/2/gT8zs50B36qyD/wncYGbbgUcIWgoiRUMD0iIikkZbdovkyMz+jmCsYLglqdPgREqaWg4iIpJGYw4iIpJGyUFERNIoOYiISBolBxERSfP/AbfRo9FyiJYmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析客厅影响\n",
    "var = 'sub_room'\n",
    "fig = sns.boxplot(x=house[var], y=house['totalprice'])\n",
    "fig.axis(ymin=0);\n",
    "plt.savefig('总价-客厅数.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = house.corr()\n",
    "f, ax = plt.subplots(figsize=(20, 9))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)\n",
    "plt.savefig('相关热力图.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalprice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalprice\n",
       "0       920.0\n",
       "1       340.0\n",
       "2       660.0\n",
       "3      1150.0\n",
       "4       570.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 切分总价\n",
    "totalprice = house['totalprice']\n",
    "totalprice = pd.DataFrame(totalprice)\n",
    "totalprice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>elevator</th>\n",
       "      <th>height</th>\n",
       "      <th>year</th>\n",
       "      <th>buildtype</th>\n",
       "      <th>city</th>\n",
       "      <th>decoration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>119.19</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>2005</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.50</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>低</td>\n",
       "      <td>2002</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.33</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>2009</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150.02</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>2008</td>\n",
       "      <td>塔楼</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109.35</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>2006</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>118.25</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>2009</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>简装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.87</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>高</td>\n",
       "      <td>2009</td>\n",
       "      <td>板楼</td>\n",
       "      <td>宝安</td>\n",
       "      <td>精装</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>93.60</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>高</td>\n",
       "      <td>2001</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>其他</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.62</td>\n",
       "      <td>有电梯</td>\n",
       "      <td>低</td>\n",
       "      <td>2007</td>\n",
       "      <td>板塔结合</td>\n",
       "      <td>宝安</td>\n",
       "      <td>其他</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>87.20</td>\n",
       "      <td>无电梯</td>\n",
       "      <td>中</td>\n",
       "      <td>1989</td>\n",
       "      <td>板楼</td>\n",
       "      <td>宝安</td>\n",
       "      <td>其他</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size elevator height  year buildtype city decoration\n",
       "0  119.19      有电梯      中  2005        塔楼   宝安         精装\n",
       "1   65.50      有电梯      低  2002      板塔结合   宝安         精装\n",
       "2   89.33      有电梯      中  2009      板塔结合   宝安         精装\n",
       "3  150.02      有电梯      中  2008        塔楼   宝安         精装\n",
       "4  109.35      有电梯      中  2006      板塔结合   宝安         精装\n",
       "5  118.25      有电梯      中  2009      板塔结合   宝安         简装\n",
       "6   30.87      有电梯      高  2009        板楼   宝安         精装\n",
       "7   93.60      有电梯      高  2001      板塔结合   宝安         其他\n",
       "8   50.62      有电梯      低  2007      板塔结合   宝安         其他\n",
       "9   87.20      无电梯      中  1989        板楼   宝安         其他"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 取X\n",
    "col = ['size', 'elevator', 'height', 'year', 'buildtype', 'city', 'decoration']\n",
    "X = pd.DataFrame(house, columns=col)\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-elevator\n",
    "import sklearn\n",
    "from sklearn import preprocessing\n",
    "le = preprocessing.LabelEncoder()\n",
    "elevator = X['elevator'].tolist()\n",
    "le.fit(elevator)\n",
    "elevator = le.transform(X['elevator'].tolist())\n",
    "elevator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 3, 3, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-decoration\n",
    "decoration = X['decoration'].tolist()\n",
    "le.fit(decoration)\n",
    "decoration = le.transform(X['decoration'].tolist())\n",
    "decoration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24, 21, 28, ..., 30, 25, 27], dtype=int64)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-year\n",
    "year = X['year'].tolist()\n",
    "le.fit(year)\n",
    "year = le.transform(X['year'].tolist())\n",
    "year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, ..., 2, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-height\n",
    "height = X['height'].tolist()\n",
    "le.fit(height)\n",
    "height = le.transform(X['height'].tolist())\n",
    "height"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1, ..., 0, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-buildtype\n",
    "buildtype = X['buildtype'].tolist()\n",
    "le.fit(buildtype)\n",
    "buildtype = le.transform(X['buildtype'].tolist())\n",
    "buildtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 2, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 标签向量化-city\n",
    "city = X['city'].tolist()\n",
    "le.fit(city)\n",
    "city = le.transform(X['city'].tolist())\n",
    "city"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 已经拆分\n",
    "# rooms = X['rooms'].tolist()\n",
    "# le.fit(rooms)\n",
    "# rooms = le.transform(X['rooms'].tolist())\n",
    "# rooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>totalprice</th>\n",
       "      <th>unitprice</th>\n",
       "      <th>position</th>\n",
       "      <th>size</th>\n",
       "      <th>orientation</th>\n",
       "      <th>decoration</th>\n",
       "      <th>elevator</th>\n",
       "      <th>area</th>\n",
       "      <th>height</th>\n",
       "      <th>year</th>\n",
       "      <th>buildtype</th>\n",
       "      <th>follow</th>\n",
       "      <th>city</th>\n",
       "      <th>main_room</th>\n",
       "      <th>sub_room</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920.0</td>\n",
       "      <td>77188</td>\n",
       "      <td>天悦龙庭二期</td>\n",
       "      <td>119.19</td>\n",
       "      <td>西南</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>0</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>340.0</td>\n",
       "      <td>51909</td>\n",
       "      <td>大益广场</td>\n",
       "      <td>65.50</td>\n",
       "      <td>西南</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>西乡</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>660.0</td>\n",
       "      <td>73884</td>\n",
       "      <td>中熙香缇湾</td>\n",
       "      <td>89.33</td>\n",
       "      <td>西南</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>西乡</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>226</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1150.0</td>\n",
       "      <td>76657</td>\n",
       "      <td>深业新岸线三期</td>\n",
       "      <td>150.02</td>\n",
       "      <td>东南</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>宝安中心</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>570.0</td>\n",
       "      <td>52127</td>\n",
       "      <td>碧海富通城一期</td>\n",
       "      <td>109.35</td>\n",
       "      <td>南</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>碧海</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>54</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   totalprice  unitprice  position    size orientation  decoration  elevator  \\\n",
       "0       920.0      77188   天悦龙庭二期   119.19          西南           3         1   \n",
       "1       340.0      51909     大益广场    65.50          西南           3         1   \n",
       "2       660.0      73884    中熙香缇湾    89.33          西南           3         1   \n",
       "3      1150.0      76657  深业新岸线三期   150.02          东南           3         1   \n",
       "4       570.0      52127  碧海富通城一期   109.35           南           3         1   \n",
       "\n",
       "   area  height  year  buildtype  follow  city  main_room  sub_room  \n",
       "0  宝安中心       0    24          0      13     1          3         2  \n",
       "1    西乡       1    21          1     160     1          2         2  \n",
       "2    西乡       0    28          1     226     1          4         2  \n",
       "3  宝安中心       0    27          0      29     1          4         1  \n",
       "4    碧海       0    25          1      54     1          3         2  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# X['orientation'] = orientation\n",
    "house['elevator'] = elevator\n",
    "house['height'] = height\n",
    "house['year'] = year\n",
    "house['buildtype'] = buildtype\n",
    "house['city'] = city\n",
    "house['decoration'] = decoration\n",
    "# X['rooms'] = rooms\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrmat = house.corr()\n",
    "f, ax = plt.subplots(figsize=(20, 9))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)\n",
    "plt.savefig('相关热力图-编码后.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k  = 10 # 关系矩阵中将显示10个特征\n",
    "cols = corrmat.nlargest(k, 'totalprice')['totalprice'].index\n",
    "cm = np.corrcoef(house[cols].values.T)\n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, \\\n",
    "                 square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.savefig('降序相关热力图.svg',format='svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>size</th>\n",
       "      <th>main_room</th>\n",
       "      <th>sub_room</th>\n",
       "      <th>year</th>\n",
       "      <th>elevator</th>\n",
       "      <th>decoration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>119.19</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>65.50</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>89.33</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150.02</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>109.35</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>118.25</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30.87</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>93.60</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>50.62</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>87.20</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     size  main_room  sub_room  year  elevator  decoration\n",
       "0  119.19          3         2    24         1           3\n",
       "1   65.50          2         2    21         1           3\n",
       "2   89.33          4         2    28         1           3\n",
       "3  150.02          4         1    27         1           3\n",
       "4  109.35          3         2    25         1           3\n",
       "5  118.25          3         2    28         1           2\n",
       "6   30.87          1         1    28         1           3\n",
       "7   93.60          3         2    20         1           0\n",
       "8   50.62          2         1    26         1           0\n",
       "9   87.20          3         2     8         0           0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = ['size', 'main_room', 'sub_room', 'year', 'elevator', 'decoration']\n",
    "X = pd.DataFrame(house, columns=col)\n",
    "X.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.21813266, 0.25      , 0.5       , 0.66666667, 1.        ,\n",
       "        1.        ],\n",
       "       [0.10384026, 0.125     , 0.5       , 0.58333333, 1.        ,\n",
       "        1.        ],\n",
       "       [0.15456829, 0.375     , 0.5       , 0.77777778, 1.        ,\n",
       "        1.        ],\n",
       "       ...,\n",
       "       [0.06469261, 0.        , 0.25      , 0.83333333, 1.        ,\n",
       "        0.        ],\n",
       "       [0.04583191, 0.        , 0.        , 0.69444444, 1.        ,\n",
       "        0.        ],\n",
       "       [0.12012517, 0.125     , 0.25      , 0.75      , 1.        ,\n",
       "        0.        ]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 归一化操作\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "MinMaxTransformer=MinMaxScaler()\n",
    "MinMaxTransformer.fit(X)\n",
    "X_transformed=MinMaxTransformer.transform(X)\n",
    "X_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test=train_test_split(X_transformed, totalprice, random_state=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[250.],\n",
       "       [800.],\n",
       "       [390.],\n",
       "       ...,\n",
       "       [665.],\n",
       "       [115.],\n",
       "       [410.]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "Xtrain = np.array(X_train)\n",
    "ytrain = np.array(y_train)\n",
    "ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\HASEE\\Anaconda3\\lib\\site-packages\\sklearn\\ensemble\\weight_boosting.py:29: DeprecationWarning: numpy.core.umath_tests is an internal NumPy module and should not be imported. It will be removed in a future NumPy release.\n",
      "  from numpy.core.umath_tests import inner1d\n",
      "C:\\Users\\HASEE\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "rf=RandomForestRegressor(n_estimators=400)\n",
    "rf.fit(X_train, y_train.astype('int'))\n",
    "predict_y=rf.predict((X_test))\n",
    "\n",
    "count = 0\n",
    "acc_count = 0\n",
    "total_error = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test = y_test['totalprice'].tolist()\n",
    "predict_y = predict_y.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "for y in y_test:\n",
    "    error = np.abs(predict_y[count] - y)\n",
    "    total_error += error\n",
    "    count += 1\n",
    "wc = total_error / (count+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143.11920592096365"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 平均误差值\n",
    "wc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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